By Lymmy Ogbidi

Abstract

As decarbonization accelerates and margins compress, the upstream sector is being forced to squeeze more value from less. Though drilling and completions are usually in the spotlight, the real operational efficiency begins below the surface, at the reservoir. In this sense, digital transformation is not following the hype of the moment, but enabling increased visibility, shorter cycle times, and safe, real-time, data-driven decision-making.

There are several aspects of this, but in the context of mature and complex fields, the integration of real-time data acquisition, forecasting, and automation has become a key driver of productivity and resilience within the field of reservoir engineering. With over 13 years of experience as a consulting reservoir simulation engineer, working with leading operators such as Shell, ExxonMobil, and TotalEnergies across Nigeria, the UK, France, and the USA, I have witnessed firsthand how digitizing reservoir workflows significantly enhances standard field management practices and accelerates decision-making processes.

Rethinking Monitoring: From Periodic Snapshots to Real-Time Insight

The traditional model of well tests, pressure build-ups, and data collection at intervals has been transformed into a much more sophisticated system of reservoir surveillance. The focus of this new paradigm is on the continuous collection of data in-situ, by means of distributed sensors, SCADA systems, and fiber-optic technologies that register real-time measurements of pressure, flow rate, water cut, temperature, and other variables.

Shell, for example, using Digital Twins and Dynamic Simulation Models for its entire global portfolio, can now calibrate the models live and control voidage proactively. These models take advantage of integration with tools such as Petrel, INTERSECT and the DELFI cognitive E&P environment by SLB which allow operators to observe the efficiency of sweeps and the behaviour of breakthroughs in near-real-time feedback loops (Schlumberger, 2023: https://www.slb.com/products/delfi-cognitive-e-and-p-environment).

An interesting case of this type was the application of real-time tracer information and voidage maps to enhance water injection in a marginal brownfield. It enabled the modification of the injection plan in a dynamic fashion, leading to a 15% increase in sweep efficiency in one quarter, which would have been a nearly impossible achievement using static surveillance tools only.

Simulation Meets Cloud: Orchestrating Multi-Scenario Decisioning

The move from closed, single-user workstation simulation software to open cloud-based collaborative environments has set a new paradigm of agility. Petrel Cloud, INTERSECT, and Ocean APIs are enabling multidisciplinary teams to execute hundreds of scenarios simultaneously, run sensitivity analyses, or data-assisted calibration routines at a level of speed never before possible.

In the case of ExxonMobil, the shift from legacy software such as EMPOWER to INTERSECT not only addressed model convergence (now greater than 40%) but also provided the capability for real-time uptake of data regionally. A shared collaborative environment across Lagos, Houston, and The Hague reduced communication time lags and allowed for more effective governance of the model.

Intelligent Forecasting: From Assisted History Matching to Predictive Modeling

A second foray into this digitalization of norms is seen in the automated history matching and AI-driven uncertainty workflows. Software such as MEPO, Petrel Uncertainty and Optimization, other industry uncertainty analysis tools, and Python-based ensembles of models are allowing engineers to automate calibration loop efforts while also measuring confidence in the predictions.

In a recent simulation campaign for the integrated development of the Isoko and Robertkiri fields in the Niger Delta, Python automation was incorporated into the history matching step of the MEPO process. This reduced manual iterating by 60% while at the same time allowing for the capture of production sensitivities within a larger parameter space to strengthen the forecasts.

Beyond that, predictive analytics is already assisting in the detection of anomalies such as undesired increases in water cut or pressure drops, which can be detected before becoming a production concern. Engineers are now more proactive in assessing mitigation strategies, whether in treatment or workover timing, supported by scenario analyses linked directly to updated models of the field.

Global Collaboration: Enabled by Integrated Digital Workflows

Digitalization has also erased distances in terms of geography. Engineers can now work continentally without losing the fidelity of work by using cloud access, shared models, and streaming data.

In a consultancy involving Shell Netherlands and Shell Brazil, calibration of the network model was managed remotely via INTERSECT, boundary condition problems were solved halfway through the cycle, and joint scenario workshops were facilitated in London. Similarly, ExxonMobil engineers in Lagos were trained to use integrated simulations developed in Houston, unifying the team in the region within a couple of weeks using the same interface and automated templates.

These workflows became not only efficient but also provided a way for local engineers to have access to high-value information and operate at the same level as global engineers using the same tools and platforms.

Looking Ahead: Reservoir Engineers as Digital Integrators

The digital transformation process is redefining the reservoir engineer’s identity at the core. The engineer of today has moved beyond merely modeling or creating static models of a reservoir; they are now a combination of data interpreter, simulator strategist, and digital integrator. The skillset has expanded to include Python scripting, automation logic, cloud interaction, and model deployment.

This transition has been repeatedly highlighted by organizations like the Society of Petroleum Engineers (SPE) and EAGE. In its 2023 report, Schlumberger states that “where asset teams have been implementing automated workflows in Petrel and DELFI, 90% report shorter cycle times and greater confidence in development planning”.

This development has a significant impact on more mature assets where there is little room for error, and variation in production can lead to dire economic consequences. Introducing live feeds from the surveillance, probabilistic forecasting, and digital twins, engineers are not just controlling the reservoirs but rather fine-tuning these systems with surgical precision.

Conclusion: Safer Decisions, Better Barrels

Reservoir management has historically been a predictive science. But in addition, it’s a science of orchestration; of insights, data streams, disciplines, and decisions that all must align to produce the best performance in the field. From deepwater Nigeria to gas basins in the North Sea or tight oil plays in North America, the integration of digital workflows in real time is now a dealbreaker for field development.
Today, engineers must not only model the subsurface but also integrate into the field’s digital nervous system. The results are impactful: faster insight, lower risk, better barrels, one scenario, one decision, one dataset at a time

About Author
Lymmy Ogbidi, Reservoir Engineering Solution Lead, SLB, United Kingdom

References

International Energy Agency (IEA). (2017). Digital transformation in oil and gas: Impact and opportunities. Periodica Polytechnica. https://www.pp.bme.hu/so/article/download/20830/9707/158933

Society of Petroleum Engineers Reservoir Advisory Committee. (2022). Reservoir technologies of the 21st century: Challenges and innovations. https://www.spe.org/media/filer_public/79/4c/794cbe46-f3b0-4a1a-b3eb-6a7c4c536941/reservoir_technologies_of_the_21st_century__05192022_updated.pdf

Bricks Technologies. (2023, November 27). Unleashing the future: Cloud-based reservoir simulation in oil and gas operations. https://brickstechnologies.ae/unleashing-the-future-cloud-based-reservoir-simulation-in-oil-and-gas-operations/

Schlumberger. (2023, September 25). Advanced logging analysis makes real-time reservoir insights a reality. SLB.
https://www.slb.com/resource-library/insights-articles/advanced-logging-analysis-makes-real-time-reservoir-insights-a-reality